ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展

所属专题: 2014智慧城市

• 人工智能 • 上一篇    下一篇

智慧城市中无线网络节点部署优化方案研究

黄书强1 王高才2 单志广3 邓玉辉4 李 阳4 陈庆麟5   

  1. 1(暨南大学网络与教育技术中心 广州 510632) 2(广西大学计算机与电子信息学院 南宁 530004) 3(国家信息中心信息化研究部 北京 100045) 4(暨南大学信息科学技术学院 广州 510632) 5(华南理工大学自动化科学与工程学院 广州 510641) (hsq2008@vip.sina.com)
  • 出版日期: 2014-02-15

Node Deployment Optimization of Wireless Network in Smart City

Huang Shuqiang1, Wang Gaocai2, Shan Zhiguang3, DengYuhui4, Li Yang4, and Chen Qinglin5   

  1. 1(Network and Education Technology Center, Jinan University, Guangzhou 510632) 2(School of Computer and Electrical Information, Guangxi University, Nanning 530004) 3(Department of Informatization Research, State Information Center, Beijing 100045) 4(College of Information and Technology, Jinan University, Guangzhou 510632) 5(School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641)
  • Online: 2014-02-15

摘要: 智慧城市无线网络基础设施中,网络节点部署直接影响到网络服务质量.该问题可归结为在给定的几何平面上部署合适的普通AP节点作为无线终端的访问节点,部署特殊节点作为网关以汇聚普通节点的流量到有线网络中.以无线Mesh网络为例,提出根据区域人流量的统计来确定AP节点的部署位置和数量,将网关节点部署问题抽象为几何K-中心问题.以节点和网关之间路径长度最小为优化目标,提出自适应的粒子群算法来求解网关节点部署位置.在自适应粒子群算法中引入随机调整惯性权重、自适应改变学习因子和邻域搜索等改进策略,并设计一种新的适值函数计算方法,使得算法更容易获得最优解.仿真结果表明,相对于GA算法和K-means算法,改进粒子群算法求解效果稳定,鲁棒性强,可获得更小的覆盖半径,从而提高网络的服务质量.

关键词: 智慧城市, 无线Mesh网络, 网关部署, 几何K-中心, 自适应粒子群算法, 路径长度

Abstract: In smart city, the deployment of network nodes of wireless networks has direct effect on network quality of service. This problem can be described as deploying appropriate AP as access nodes and special nodes as gateway nodes to aggregate traffic to Internet in a given geometric plane. In the paper, wireless mesh network as an example, number and deployment location of AP nodes can be determined by the regional flow of people statistics, and gateway nodes deployment is abstracted as a geometric K-center problem. To solve the geometric K-center problem, an improved adaptive PSO algorithm is proposed to optimize the minimum coverage radius. The fitness function is redesigned, and random inertia weight adjustment, adaptive learning factor, neighborhood searching strategy are introduced to the improved PSO to get wider solution. Compared with GA algorithm and K-means algorithm, simulation results show that the improved PSO algorithm is more stable and can get shorter path length, thus the network quality of service can be improved.

Key words: smart city, wireless mesh networks, gateway deployment, geometric K-center, adaptive PSO, path length